Quantitative proteomic analysis reveals that lipopolysaccharide induces mitogen‐activated protein kinase‐dependent activation in human microglial cells
Bibliographic record
Abstract
Microglial cells act as the first and main form of active immune defense in the central nervous system related to inflammation and neurodegenerative disease. Lipopolysaccharide (LPS) induces many genes encoding inflammatory mediators, including cytokines such as tumor necrosis factor-α, interleukin-1β, (IL-1β), and IL-6, chemokines, and prostaglandins in microglial cells. Quantitative proteomics methods with isobaric chemical labeling using tandem mass tags and 2D-nano LC-ESI-MS/MS were used to systematically analyze proteomic changes in microglia responding to LPS stimulation. As a result, we found that the expression level of 21 proteins in human microglial cells changed after activation. Among those, one of the strong mitogen-activated protein kinase (MAPK) regulator proteins, CMPK1 was highly upregulated after LPS stimulation in human microglial cells. We detected and validated upregulation of MAPK including ERK1/2, p38, and SAPK/JNK by immunohistochemistry and Western blotting. NFκB, strong transcription factor of CMPK1, was translocated to the nucleus from the cytosol by high contents screening after LPS stimulation. Taken together, we conclude that MAPK signaling plays an important role in LPS-induced human microglial activation related to inflammatory response.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".